diff --git a/include/tkDNN/SegmentationNN.h b/include/tkDNN/SegmentationNN.h
index fff27a8..403bc28 100644
--- a/include/tkDNN/SegmentationNN.h
+++ b/include/tkDNN/SegmentationNN.h
@@ -18,7 +18,6 @@
 #include "tkdnn.h"
 #include "NetworkViz.h"
 #include "kernelsThrust.h"
-#define SLAM_MODE
 
 namespace tk { namespace dnn {
 
@@ -100,62 +99,6 @@ class SegmentationNN {
          * 
          * @param bi batch index
          */
-
-        #ifdef SLAM_MODE
-        cv::Mat postprocess(const int bi=0,bool apply_colormap=true){
-            cv::Mat maskMatrix;
-            dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
-
-            dataDim_t odim = netRT->output_dim;
-            
-            matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
-            maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
-            checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
-
-
-
-            dataDim_t vdim = odim;
-            vdim.c = 1;
-            dnnType *dataTemp = nullptr;
-            if(isCudaPointer(tmpOutData_h))
-            {
-                dataTemp = new dnnType[vdim.tot()];
-                checkCuda(cudaMemcpy(dataTemp,tmpOutData_h,vdim.tot()*sizeof(dnnType),cudaMemcpyDeviceToHost));
-            }
-            else
-            {
-                dataTemp = tmpOutData_h;
-            }
-            for(int i =0;i<vdim.c;i++){
-            maskMatrix = cv::Mat(originalSize[bi],CV_32FC1,dataTemp + vdim.h*vdim.c*i);
-            }
-            cv::Mat colored;
-
-            if(apply_colormap)
-                colored = vizData2Mat(dataTemp, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
-            else{
-                cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, dataTemp);
-                colored_fp32.convertTo(colored, CV_8UC1);
-            }
-            
-            int max_dim = (originalSize[bi].width > originalSize[bi].height) ? originalSize[bi].width : originalSize[bi].height;
-            resize(colored, colored, cv::Size(max_dim, max_dim));
-            int top, bottom, left, right;
-            computeBorders(originalSize[bi].width, originalSize[bi].height, top, bottom, left, right);
-            cv::Rect roi(left,top,originalSize[bi].width, originalSize[bi].height);
-            cv::Mat or_size (colored, roi);
-            segmented[bi] = or_size;
-
-            if(isCudaPointer(tmpOutData_h))
-            {
-                delete [] dataTemp;
-            }
-
-            return maskMatrix;
-
-        }
-        #elif
-        
         void postprocess(const int bi=0, bool appy_colormap = true) {
             dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;
 
@@ -185,7 +128,6 @@ class SegmentationNN {
             cv::Mat or_size (colored, roi);
             segmented[bi] = or_size;
         };
-        #endif 
 
     public:
         int classes = 0;
@@ -295,184 +237,6 @@ class SegmentationNN {
             }
         }     
 
-        #ifdef SLAM_MODE
-        cv::Mat updateOriginal(cv::Mat frame,bool apply_colormap=true){
-            std::vector<cv::Mat> splitted_frames;
-            cv::Mat maskMatrix;
-            int H, W, net_H, net_W;
-            int top = 0, bottom = 0, left = 0, right = 0;
-            std::vector<std::pair<int,int>> pos;
-
-            {
-                TKDNN_TSTART
-                cv::Size original_size =  frame.size();
-
-                frame.convertTo(frame, CV_32FC3, 1 / 255.0, 0);
-                H = frame.rows;
-                W = frame.cols;
-                net_H = netRT->input_dim.h; 
-                net_W = netRT->input_dim.w;
-
-                cv::Mat frame_cropped;
-                
-                if( H <= net_H && W <= net_W ){ // smaller size wrt network
-                    top = (net_H - H)/2;
-                    bottom = net_H - H - top ;
-                    left = (net_W - W)/2;
-                    right = net_W - W - left ;
-                    cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0) );
-                    splitted_frames.push_back(frame_cropped);
-                }
-                else{ //bigger size wrt network
-
-                    
-                    if(H < net_H  ||  W < net_W){
-                        if(H < net_H){
-                            top = (net_H - H)/2;
-                            bottom = net_H - H - top ;
-                        }
-                        else{
-                            left = (net_W - W)/2;
-                            right = net_W - W - left ;
-                        }
-                        cv::copyMakeBorder(frame, frame_cropped, top, bottom, left, right, cv::BORDER_CONSTANT, cv::Scalar(0,0,0));
-                    }
-
-                    for(int x=0; x+net_W<=W ;){
-                        for(int y=0; y+net_H <=H ; ){
-                            cv::Rect roi(x, y, net_W, net_H);
-                            cv::Mat image_roi = frame(roi);
-                            splitted_frames.push_back(image_roi);
-                            pos.push_back(std::make_pair(x,y));
-                            
-                            y += net_H;
-                            if(y == H)
-                                break;
-                            if(y + net_H > H) y = H - net_H;
-                        }
-                        x += net_W;
-                        if(x == W)
-                            break;
-                        if(x + net_W > W) x = W - net_W;
-                    }
-                }
-
-                tk::dnn::dataDim_t idim = netRT->input_dim;
-
-                if(splitted_frames.size()> nBatches)
-                    FatalError(std::to_string(splitted_frames.size()) + " min batches required");
-
-                for(int bi=0; bi<splitted_frames.size();++bi){
-                    cv::split(splitted_frames[bi], bgr);
-                    for (int i = 0; i < idim.c; i++){
-                        int idx = i * splitted_frames[bi].rows * splitted_frames[bi].cols;
-                        int ch = idim.c-1 -i;
-                        memcpy((void *)&input[idx + idim.tot()*bi], (void *)bgr[ch].data, splitted_frames[bi].rows * splitted_frames[bi].cols * sizeof(dnnType));
-                    }
-
-                    checkCuda(cudaMemcpyAsync(input_d+ idim.tot()*bi, input + idim.tot()*bi, idim.tot() * sizeof(dnnType), cudaMemcpyHostToDevice, netRT->stream));
-                    normalize(input_d + idim.tot()*bi, idim.c, idim.h, idim.w, mean_d, stddev_d);
-                }
-                TKDNN_TSTOP
-                stats_pre.push_back(t_ns);
-            }
-
-            tk::dnn::dataDim_t dim = netRT->input_dim;
-            dim.n = splitted_frames.size();
-            {
-                if(TKDNN_VERBOSE) dim.print();
-                TKDNN_TSTART
-                netRT->infer(dim, input_d);
-                TKDNN_TSTOP
-                if(TKDNN_VERBOSE) dim.print();
-                stats.push_back(t_ns);
-            }
-
-            dataDim_t odim = netRT->output_dim;
-
-            std::vector<cv::Mat> out_img;
-            std::vector<cv::Mat> out_mask;
-
-            {
-                TKDNN_TSTART
-
-                for(int bi=0; bi<splitted_frames.size();++bi){
-
-                    dnnType *rt_out = (dnnType *)netRT->buffersRT[1]+ netRT->buffersDIM[1].tot()*bi;               
-                    
-                    matrixTranspose(cublasHandle, rt_out, tmpInputData_d, odim.c, odim.w*odim.h);
-                    maxElem(tmpInputData_d, tmpOutData_d, odim.c, odim.h, odim.w);
-                    checkCuda(cudaMemcpy(tmpOutData_h, tmpOutData_d, odim.w*odim.h * sizeof(float), cudaMemcpyDeviceToHost));
-
-                    dataDim_t vdim = odim;
-                    vdim.c = 1;
-                    dnnType *dataTemp = nullptr;
-                    if(isCudaPointer(tmpOutData_h))
-                    {
-                        dataTemp = new dnnType[vdim.tot()];
-                        checkCuda(cudaMemcpy(dataTemp,tmpOutData_h,vdim.tot()*sizeof(dnnType),cudaMemcpyDeviceToHost));
-                    }
-                    else
-                    {
-                     dataTemp = tmpOutData_h;
-                    }
-    
-                    cv::Mat colored;
-                    for(int i=0;i<vdim.c;i++){
-                    out_mask.push_back(cv::Mat(cv::Size(odim.w,odim.h),CV_32FC1,dataTemp + dim.h*dim.c*i));
-                    }
-
-
-                    if(apply_colormap)
-                        colored = vizData2Mat(tmpOutData_h, vdim, netRT->input_dim.h, netRT->input_dim.w, 0, classes, classes);
-                    else{
-                        cv::Mat colored_fp32 (cv::Size(odim.w, odim.h),CV_32FC1, tmpOutData_h);
-                        colored_fp32.convertTo(colored, CV_8UC1);
-                    }
-                    out_img.push_back(colored);
-                    if(isCudaPointer(tmpOutData_h))
-                    {
-                       delete [] dataTemp;
-                    }
-                    }
-
-                cv::Mat tempMask(frame.size(), out_mask[0].type());
-                cv::Mat seg(frame.size(), out_img[0].type());
-                if(out_img.size() == 1)
-                {
-                    cv::Rect roi(left, top, W, H);
-                    seg = out_img[0](roi);
-                    tempMask = out_mask[0](roi);
-                }
-                else{
-                    int bi=0;
-
-                    if(top == 0 && left == 0){
-
-                        for(int i=0; i<out_img.size(); ++i){
-                            cv::Mat roi_collage = seg(cv::Rect( pos[i].first ,pos[i].second,out_img[i].cols,out_img[i].rows));
-                            cv::Mat roi_collage2 = tempMask(cv::Rect( pos[i].first ,pos[i].second,out_img[i].cols,out_img[i].rows));
-
-                            out_img[i].copyTo(roi_collage);
-                            out_mask[i].copyTo(roi_collage2);
-                        }
-                    }
-                    else{
-                        FatalError("Not handled case")
-                    }
-                }
-                maskMatrix = tempMask;
-                segmented[0] = seg;
-                
-                TKDNN_TSTOP
-                stats_post.push_back(t_ns);
-                
-                
-            }
-            return maskMatrix;
-
-        }
-        #elif
         void updateOriginal(cv::Mat frame, bool apply_colormap=true){
 
             std::vector<cv::Mat> splitted_frames;
@@ -621,11 +385,6 @@ class SegmentationNN {
                 stats_post.push_back(t_ns);
             }
         } 
-        #endif 
-        
-
-
-
 
         /**
          * Method to draw boundixg boxes and labels on a frame.